I think the authors chose the wrong narrative to discuss in their motivation for the paper. Anyway, it's easier to just shoot a movie / VR content with multiple

I think the authors chose the wrong narrative to discuss in their motivation for the paper. Anyway, it's easier to just shoot a movie / VR content with multiple cameras than go through frame-by-frame to make sure the AI isn't introducing blurry regions etc. However, being able to extract 3D scene features from a still 2D image is a super useful feature to have. Perhaps in the near future, inference models for image-based tasks (i.e. ImageNet, robotics) will make use of 3D features learned during end-to-end training. The disparity predictions generated here remind me of spatial transformer layers http://arxiv.org/abs/1506.02025 http://arxiv.org/abs/1506.02025 which Deepmind is using pretty heavily in their sequential attention models. I wonder if they would offer an improvement over the deconv layers.